Remote Sensing and Apparent Electrical Conductivity to Characterize Soil Water Content.

Improvement in water use efficiency of crops is a key component addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining amount of water to be applied to irrigated crops. Neutron probes (NPs) have consistently been used in studies as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of LAI and biomass, which are related with root water uptake. Crop yield has not been evaluated on a basis of SWC as explained by NPs in time and at different depths. The objectives of this study were (i) to determine the optimal time and depth of SWC and its relationship to maize grain yield, and (ii) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. This study was conducted on maize (Zea Mays L.) irrigated in two fields in northern Colorado. Soil water data was collected at five soil depths (30, 60, 90, 120 and 150 cm), 21 and 12 times at Site I and II, respectively. Three vegetation indices were calculated on seven dates (Emergence to R3). Grain yield was harvested at physiological maturity at each NPs location. Automated model selection of SWC readings to assess maize yield consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). Readings at 90 cm depth had the highest correlations with yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health situation right before the reproductive stage. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool of significant help for maize irrigation management.